This project will utilize geographic analysis techniques to detect the influence of key aspects of the local social and built environment on metabolic syndrome related disorders at the individual level. The specific community characteristics that these measures will be constructed for are 1) geographic access to primary care, 2) local nutritional contexts, and 3) proximity to public opportunities for exercise. The study will test the following hypotheses: Hypothesis 1: Self reported metabolic syndrome related behaviors (levels of physical activity and nutritional behaviors) are independently influenced by geographic relationships to local nutritional outlets and proximity to public opportunities for exercise even after controlling for known predisposing, enabling and need based factors, along with perceptions of public safety. Hypothesis 2: Self reported metabolic syndrome related outcomes (hypertension, diabetes, cardiovascular disease etc...) are independently influenced by geographic relationships to local nutritional outlets, and proximity to public opportunities for exercise even after controlling for known predisposing, enabling and need based factors, along with perceptions of public safety. Hypothesis 3: The geographic relationships observed in the self reported data will correlate strongly with those observed for small area population centers and morbidity and mortality data. The analysis will proceed as follows: a) nesting Los Angeles Health Survey respondents within their local health care accessibility contexts by utilizing street network based gravity models as indicators of geographic accessibility to primary care providers, b) nesting Los Angeles Health Survey respondents within their local food availability contexts by utilizing street network based gravity models to characterize local access to health promoting and health inhibiting food outlets (fresh produce vs. fast food), derived from geo-coded food license data, c) nesting Los Angeles Health Survey respondents within their local public exercise space contexts using geographic data on parks and open space to derive street network based gravity models of geographic accessibility to land uses that have been shown to influence levels of physical activity within a neighborhood. These predictor variables will be incorporated within a hierarchical model that accounts for the known influences on health care utilization and the use of public facilities. The results from the self reported data will be validated using small area data on morbidity and mortality Public Health Relevance: One of the major obstacles confronting researchers interested in the translation of chronic disease research to clinical practice is a lack of knowledge concerning the barriers and facilitators to research translation that exist in different types of social and built environments. The proposed research will utilize geographic theory and analysis techniques to detect the influence of local community environment and structure on individual health related behaviors and outcomes. Specifically, we will examine the influence of 1) geographic access to primary care, 2) local nutritional contexts, and 3) proximity to public opportunities for exercise, on self reported health behaviors and outcomes related to Metabolic Syndrome.